Systems, methods, and devices for monitoring content viewership using short-range wireless communication

ABSTRACT

Systems, methods, and computer-readable medium are provided for monitoring content viewership using short-range wireless communication by transmitting a short-range wireless signal, detecting a user device that responds to the transmitting, detecting content being presented by a media device, storing monitoring data that includes an indication of the user device and an indication of the content being presented, and transmitting the monitoring data to a server. Systems, methods, and computer-readable medium are also provided for receiving monitoring data from multiple monitoring devices, determining users associated with user devices that were detected, identifying content based on indications of content detected by the monitoring devices, matching the users to the content using the monitoring data, and generating a report of content viewership based on the matching.

BACKGROUND

Monitoring viewership of broadcast media content, such as televisionprogramming, has been a difficult and imperfect process. Traditionally,target audiences would have to self-record and report their own viewingor listening habits to audience measurement systems.

Improvements in technology have led to the use of specialized monitoringdevices that can be connected to home televisions to record and reportthe station or program that a television is tuned to, which approximatesthe viewing habits of participating viewers. However, a household mayhave multiple viewers that use the same television, and the specializedmonitoring devices are incapable of determining which viewers (if any)are actually watching the television and/or at what times or during whatperiod viewers are in the room. While conventional monitoring devicescan perhaps determine when a television is tuned to a specific programand for how long, such devices cannot determine which of all thepossible viewers is, or could be, in fact viewing the program and cannotdetermine the time period for which each possible viewer viewed, orcould have viewed, the program.

For example, a household may have several viewers that cover multipledemographics, such as adult men, adult women, teenagers, and youngchildren. Determining which of the viewers are actually watching thetuned-to content and matching specific viewers and viewing periods tospecific content can greatly improve content viewership measurements.

Therefore, there is a desire for systems, devices, and methods forproviding improved monitoring of content viewership.

SUMMARY

Systems, apparatus, computer-readable media, and methods are disclosedfor monitoring content viewership using short-range wirelesscommunication by transmitting a short-range wireless signal, detecting auser device that responds to the transmitting, detecting content beingpresented by a media device, storing monitoring data that include anindication of the user device and an indication of the content beingpresented, and transmitting the monitoring data to a server.

Systems, apparatus, computer-readable media, and methods are alsodisclosed for monitoring content viewership using short-range wirelesscommunication by receiving monitoring data from multiple monitoringdevices, where the monitoring data includes indications of user devicesthat were detected by the monitoring devices using a short-rangewireless signal and indications of content presented by media devicesthat was detected by the monitoring devices, determining usersassociated with the user devices that were detected, identifying contentbased on the indications of the content detected by the monitoringdevices, matching the users to the content using the monitoring data,and generating a report of content viewership based on the matching.

It will be appreciated that this summary is intended merely to introducea subset of aspects of the disclosure, presented below. Accordingly,this summary is not to be considered limiting on the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various embodiments of thepresent disclosure and together, with the description, serve to explainthe principles of the present disclosure. In the drawings:

FIG. 1 is a diagram depicting an example of a content and usermonitoring environment, consistent with certain disclosed embodiments;

FIG. 2 is a diagram depicting an example of a content and usermonitoring system, consistent with certain disclosed embodiments;

FIG. 3 is a flow diagram illustrating an example of a process ofmonitoring user devices and content, consistent with certain disclosedembodiments;

FIG. 4 is a flow diagram illustrating an example of a process ofanalyzing monitoring data, consistent with certain disclosedembodiments;

FIG. 5 is a flow diagram illustrating an example of a process ofmonitoring user devices and content and analyzing monitoring data,consistent with certain disclosed embodiments;

FIG. 6 is a flow diagram illustrating an example of a process ofanalyzing monitoring data that includes content viewership data frommonitoring devices, consistent with certain disclosed embodiments;

FIG. 7 is a flow diagram illustrating an example of a process ofdetermining adjusted content viewership data, consistent with certaindisclosed embodiments; and

FIG. 8 is a diagram illustrating an example of a hardware system thatmay be used to implement a content and user monitoring system,consistent with certain disclosed embodiments.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever convenient, the same reference numbers are used in the drawingsand the following description refers to the same or similar parts. Whileseveral examples of embodiments and features of the present disclosureare described herein, modifications, adaptations, and otherimplementations are possible, without departing from the spirit andscope of the present disclosure. Accordingly, the following detaileddescription does not limit the present disclosure. Instead, the properscope of the disclosure is defined by the appended claims.

Content viewership monitoring technologies generally are unable to matchspecific viewers and viewing periods to specific content, particularlyin multi-viewer households. Additionally, targeted advertisingtechnologies are generally limited to delivering advertisements based onoverall geographic or household demographics and/or the demographics ofall users of a media device, and may be unable to target specificviewers.

As described below, various technologies, including content viewershipmonitoring technologies and targeted advertising technologies, can beimproved by providing real-time content viewership measurements thatmatches specific viewers and viewing periods to specific content.Content viewership measurements can be captured in real-time bymonitoring user devices, such as smartphones, which are generallyoperated by a single viewer. Many user devices include hardware thatallows for short-range wireless communication, such as a radio frequencytransmitter. Accordingly, short-range wireless communication can beleveraged to monitor locations of user devices and, thus, the operatorsof the user devices. For example, short-range wireless communication canbe used to detect when a user device is in the same room as a monitoringdevice.

The user device location information can be combined with monitoredcontent information (e.g., collected using a microphone, a video camera,a connection, etc. as described below), and specific viewers and viewingperiods can be matched to specific content in real time. The real-timecontent viewership can be used to generate reports (e.g., in real time),adjust advertising rates (e.g., in real time), and/or selectadvertisements targeted for specific viewers (e.g., in real time).

FIG. 1 is a diagram depicting an example of a content and usermonitoring environment, consistent with certain disclosed embodiments.In particular, FIG. 1 depicts a content and user monitoring environment100 that includes a user device 110, a user device 120, a user device130, a media device 140, and a monitoring device 150.

In some embodiments, user device 110, user device 120, and user device130 can each represent any type of one or more portable computingdevices that can exchange data over short distances using wirelesstechnology. For example, user device 110, user device 120, and/or userdevice 130 can transmit and receive signals that correspond to theBluetooth® wireless technology standard (i.e., using short-wavelengthultra-high frequency (UHF) radio waves in the industrial, scientific,and medical (ISM) band from 2.4 to 2.485 gigahertz (GHz)). Hereinafter,a signal that corresponds to the Bluetooth® wireless technology standardcan be referred to as a Bluetooth® signal. In another example, userdevices 110-130 may transmit and receive signals using a line-of-sightwireless technology, such as infrared signaling.

In various embodiments, user device 110, user device 120, and/or userdevice 130 can be, for example, a smartphone, a tablet computer, aportable media player, a laptop, a laptop/tablet computer hybrid, asmartwatch, an activity tracker, a Bluetooth tracker, etc. For example,each participating member of a participating household can be providedwith a Bluetooth tracker (e.g., a Tile™ Bluetooth tracker) that theycarry around with them while in their house, including when they watchtelevision, listen to the radio, etc. The Bluetooth tracker can becapable of receiving inquiry requests and transmitting responses thatinclude an address of the Bluetooth tracker, a name of the Bluetoothtracker, and/or a user identifier associated with a particular one ofthe users. Inquiry requests and responses are discussed in furtherdetail below.

In some implementations, user device 110, user device 120, and/or userdevice 130 can be a device capable of running a content viewershipmonitoring application, such as a smartphone, a tablet computer, alaptop, a laptop/tablet computer hybrid, etc. In such implementations,users that have elected to participate in content viewership monitoringcan install the content viewership monitoring application on his or heruser device 110-130. The application can allow the user, in someembodiments, to register for content viewership monitoring by enteringhis or her name and demographic information. The user's name anddemographic information and an identifier of the user device running theapplication (e.g., an address of the user device) can be included in aroster of participating users, discussed in further detail below.

In other implementations, participating users can register for contentviewership monitoring using other methods, such as, for example,registering and providing their name, demographic information, and userdevice identifier via a website or via a mail-in form.

In some embodiments, the application can allow the user to indicate whenthey are consuming content, such as watching television or listening tothe radio, (hereinafter referred to as “viewing” content, which includeslistening to audio-only content). Accordingly, one or more user devicescan be matched to the user using a clustering analysis, discussed infurther detail below. For example, the user can indicate when they areviewing content via a smartphone and the system (e.g., the application)may match or associate the user to the smartphone, and the system mayalso match or associate the user to a smartwatch that is often ortypically within signal range of monitoring device 150 when the user isviewing content.

In various embodiments, the application can determine which user isassociated with a user device using a voice recognition algorithm. Forexample, the application can capture a sample of the user's voice usinga microphone of the user device and transmit the recorded audio and/orvoice biometrics to a central server (not shown in FIG. 1). The centralserver can store voice biometrics associated with participating usersfor matching user devices to participating users, determine voicebiometrics associated with an audio file, and/or determine participatingusers using the voice biometrics.

In some embodiments, media device 140 can represent any type of one ormore devices that can present content (e.g., audio content, audiovisualcontent, etc.) such as a television, a radio, a video game console, orthe like. In further embodiments, the content can be media content, suchas television or radio programming or the like. As used herein, the“content’ presented by a media device can include, but is not limitedto, songs, radio shows, television shows, movies, sporting events, audiocommercials or advertisements, video commercials or advertisements,still-picture advertisements, etc., regardless of the conveying means,which may include RF broadcasted content, cable content, satellitecontent, pay-per-view content, on-demand content, etc.

In some embodiments, monitoring device 150 can represent any type of oneor more devices that can exchange data over short distances usingwireless technology and store monitoring information. For example,monitoring device 150 can be a Bluetooth scanner and/or beacon that isable to receive and transmit Bluetooth signals. In another example,monitoring device 150 may be a computerized device capable of infraredsignaling. In various embodiments, monitoring device 150 can exchangedata with one or more user devices (e.g., user device 110 and userdevice 120) that are within range.

In some implementations, monitoring device 150 can exchange data bytransmitting a low power signal (e.g., a one-half to one milliwatt (mW)radio frequency (RF) signal) to identify any user devices within therange of the low power signal (e.g., five to ten meters, or within thesame room). In various embodiments, the data exchanged betweenmonitoring device 150 and any user devices can include a user deviceidentifier and/or a user identifier. In some embodiments, the userdevice identifier can be a unique address associated with a user device.For example, Bluetooth® RF devices are associated with a unique 48-bitaddress presented in the form of a 12-digit hexadecimal value, and theunique 48-bit address can be the user device identifier. In otherembodiments, the user identifier can be a name, username, and/or anyother type of unique identifier associated with a user of the userdevice.

Signal range 160, can represent a distance or a range in whichmonitoring device 150 is capable of reliably exchanging data with userdevices. As shown in FIG. 1, user device 110 and user device 120 arewithin signal range 160. Accordingly, monitoring device 150 is capableof exchanging data with user device 110 and user device 120 for thepurpose of monitoring the user devices. As also shown in FIG. 1, userdevice 130 is outside of signal range 160. Accordingly, user device 130may not be able to receive a signal from monitoring device 150 (and/orvice versa) that is of sufficient strength to reliably exchange data.For implementations employing a line-of-sight signaling technology, thesignal range 160 may conform to the shape of the room containing themonitoring device 150.

In some embodiments, monitoring device 150 can determine which devicesare within signal range 160. For example, monitoring device 150 cantransmit an inquiry request using the low power signal. If user device110 and/or user device 120 are listening for inquiry requests, they canreceive the transmitted request, and respond by transmitting an addressof the user device, a name of the user device, and/or a user identifier.In some embodiments, the transfer of requests and responses can occurwithout a formal connection being established between monitoring device150 and user device 110 and/or user device 120. In other embodiments,monitoring device 150 can connect to the user devices, and the userdevices can transmit information, such as a name of the user deviceand/or a user identifier, while connected to monitoring device 150. Invarious embodiments, monitoring device 150 can transmit inquiry requestsat regular intervals (e.g., every fifteen seconds) to determine whichuser devices are currently within signal range 160.

As shown in the example of FIG. 1, because user device 130 is outside ofsignal range 160, user device 130 may not receive the inquiry requestand, accordingly, may not respond with its address, name, and/or a useridentifier.

In some implementations, monitoring device 150 and user device 110, userdevice 120, and/or user device 130 can use the leader/follower model ofcommunication, where monitoring device 150 (i.e., the leader device) hasunidirectional control over user device 110, user device 120, and/oruser device 130 (i.e., the follower devices). Accordingly, monitoringdevice 150 can be connected to multiple leader devices, but each userdevice can only be connected to a single follower device and are notconnected directly to each other.

A network that uses Bluetooth® signals and the leader/follower model ofcommunication is generally referred to as a “piconet.” The piconet caninclude only one leader device and one or more follower devices.Generally, in Bluetooth® protocols, the leader device can interconnectwith up to seven active follower devices. However, the leader device canmanage additional inactive follower devices and bring them into activestatus at any time.

In some embodiments, monitoring device 150 can additionally include amicrophone 154 and/or a video camera 152 to capture audio and/or visualoutput from media device 140. In some implementations, monitoring device150 can store audio and/or audiovisual files by recording output frommedia device 140. In further implementations, monitoring device 150 candetect or identify acoustic, video, and/or image fingerprints and/ordigital watermarks in the stored audio and/or audiovisual files. Invarious embodiments, monitoring device can capture audio and/oraudiovisual output at regular intervals (e.g., every thirty seconds).

For example, monitoring device 150 can employ automatic contentrecognition (ACR) to identify acoustic and/or video fingerprints in arecorded audio or audiovisual file. An acoustic fingerprint is acondensed digital summary, deterministically generated from an audiosignal, that can be used to identify an audio sample or quickly locatesimilar items in an audio database. In some embodiments, monitoringdevice 150 can have the audio database stored thereon, and can updatethe audio database via communication with a central server. The audiodatabase can be used to associate acoustic fingerprints with content. Inother embodiments, monitoring device 150 can send the acousticfingerprint to a central server for identification of the content. Infurther embodiments, monitoring device 150 can simply store the acousticfingerprint for later analysis and identification or determination ofthe content by a central server, as discussed below.

As an additional example, monitoring device 150 can use ACR to identifydigital watermarks in an audio or audiovisual file. A digital watermarkis a marker covertly embedded in a noise-tolerant signal such as asignal from the media device 140 as recorded in an audio, audiovisual,or image file. A digital watermark may be invisible to a person watchingor listening to the content, but can be captured and identified orrecognized by monitoring device 150 by applying a digital watermarkdetecting and retrieval algorithm to an audio or audiovisual file. Adigital watermark must be embedded using an digital-watermark-embeddingalgorithm on an audio or audiovisual file prior the content beingbroadcast (e.g., by the content creators, by a television station airingthe content, by a radio station airing the content, etc.). The digitalwatermark can be associated with the content and the association can bestored in a digital watermark database. In some embodiments, monitoringdevice 150 can have the digital watermark database stored thereon, andcan update the digital watermark database to keep it current bycommunication with a central server. In other embodiments, monitoringdevice 150 can send the identified digital watermark to a central serverfor identification of the content. In further embodiments, monitoringdevice 150 can simply store the digital watermark for later analysis andidentification or determination of the content by a central server, asdiscussed below.

In still further implementations, monitoring device 150 can determinecontent being presented by media device 140 by communicating with mediadevice 140. For example, monitoring device 150 can be connected to mediadevice 140 (e.g., via a direct network connection, via a local areanetwork connection, via a jumper cable, and/or via a wireless signal orthe like) and receive indications of the channel and/or station thatmedia device 140 is tuned to. As another example, monitoring device 150can be incorporated into and a part of media device 140 (i.e.,monitoring device 150 and media device 140 can be one device), and canreceive indications of and/or be configured to monitor the channeland/or station to which media device 140 is tuned.

In various embodiments, monitoring device 150 can link, connect, orotherwise associate content with user device identifiers (e.g., anaddress), user device names, and/or user identifiers. For example, inthe event that a user device is within signal range 160 at the same timeas content is being presented, the user device and/or user can beassociated with the content, for example by creating and/or storinginformation or a record of this event. In some embodiments, monitoringdevice 150 can associate the content, the user device identifiers, theuser device names, and/or the user identifiers with timestamps, and sendinformation corresponding to the content, the user device identifiers,the user device names, and/or the user identifiers along with thetimestamps to a central server, as described in further detail below.

The diagram depicted in FIG. 1 is merely for the purpose of illustrationand is not intended to be limiting. Further, the content and usermonitoring environment depicted is merely a simplified example of acontent and user monitoring environment, consistent with certaindisclosed embodiments, but this example is not intended to be limitingand many variations are possible. For example, in various embodiments,the content and user monitoring environment can include additional userdevices, media devices, monitoring devices, and/or other devices.

FIG. 2 is a diagram depicting an example of a content and usermonitoring system, consistent with certain disclosed embodiments. Inparticular, FIG. 2 depicts a content and user monitoring system 200 thatincludes a monitoring device 210, a monitoring device 220, a monitoringdevice 230, a server 240, and a network 250.

In some embodiments, monitoring device 210, monitoring device 220,and/or monitoring device 230 can each represent any type of one or moredevices that can exchange data over short distances using wirelesstechnology, store monitoring data, and send monitoring data to server240 via network 250. For example, monitoring device 210, monitoringdevice 220, or monitoring device 230 can represent monitoring device 150in FIG. 1.

In some implementations, server 240 can represent any type of one ormore computing devices that can, for example, communicate with otherdevices via network 250, analyze monitoring data from one or moremonitoring devices, and generate monitoring reports. In variousembodiments, server 240 can represent one or more computing devices suchas, for example, a server, a mainframe computer, a laptop computer,and/or a desktop computer. In some embodiments, server 240 can generatereal-time content viewership that can be used to generate reports (e.g.,in real time), adjust advertising rates (e.g., in real time), and/orselect advertisements targeted for specific viewers (e.g., in realtime).

In some embodiments, the monitoring data can include audio oraudiovisual files recorded by monitoring device 210, monitoring device220, or monitoring device 230. In further implementations, themonitoring data can include acoustic fingerprints or digital watermarksgenerated by and received from monitoring device 210, monitoring device220, or monitoring device 230 or acoustic fingerprints or digitalwatermarks generated by server 240 based on the stored audio oraudiovisual files. In still further implementations, server 240 canidentify content associated with the audio or audiovisual files oracoustic fingerprints or digital watermarks by analyzing the acousticfingerprints or digital watermarks, while, in additionalimplementations, the monitoring data received from monitoring device210, monitoring device 220, and/or monitoring device 230 can includecontent identifiers. In various embodiments, server 240 can store thecontent identifiers.

In some implementations, the monitoring data can include indications ofuser devices and/or indications of users associated with the content. Insome embodiments, where the monitoring data includes indications of userdevices, server 240 can compare the indications of user devices (e.g.,addresses, user device names, etc.) to a stored roster of participatingusers that associates user devices with participating users.Accordingly, server 240 can match users to user devices, and determinethe users associated with the content.

In other implementations, the monitoring data can include indications ofuser devices and/or indications of users associated with timestamps. Themonitoring data can also include content information associated withtimestamps. In such embodiments, server 240 can match users to contentusing the timestamps. In other words, server 240 can determine that auser was watching certain content if the timestamp associated with theuser's device matches a timestamp associated with the content.

In some embodiments, the monitoring data can additionally include aunique identifier of the monitoring device that generated the monitoringdata, such as, for example, an alphanumeric string that is unique toeach monitoring device, so that server 240 can determine whichmonitoring device is associated with the monitoring data.

In further embodiments, server 240 can verify the content information bycomparing the timestamp in the content information to a programmingschedule. Additionally, if the content information only includes anindication of a channel that a media device was tuned to, server 240 canidentify the content based on the timestamp and the schedule.

In some embodiments, network 250 can represent any type of one or morewide area communications networks. For example, network 250 can includethe Internet and/or one or more mobile networks.

In some embodiments, server 240 can additionally transmit information tomonitoring devices 210-230, such as, for example, a roster ofparticipating users and updates thereof, an audio database and updatesthereof, a digital watermark database and updates thereof, etc.

The diagram depicted in FIG. 2 is merely for the purpose of illustrationand is not intended to be limiting. Further, the content and usermonitoring system depicted is merely a simplified example of a contentand user monitoring system, consistent with certain disclosedembodiments, but this example is not intended to be limiting and manyvariations are possible. For example, in various embodiments, thecontent and user monitoring system can include additional monitoringdevices, servers, and/or other devices.

FIG. 3 is a flow diagram illustrating an example of a process ofmonitoring user devices and content, consistent with certain disclosedembodiments. In various embodiments, the process can be performed usinga computerized monitoring device or devices. For example, the processcan be performed by monitoring device 150 in FIG. 1 or monitoring device210, monitoring device 220, or monitoring device 230 in FIG. 2.

The example of a process shown can begin in 300, when the monitoringdevice detects one or more user devices. For example, the monitoringdevice can transmit an inquiry request using a low power (e.g., weak)signal (e.g., a one mW signal) to identify any user devices within arange of the low power signal (e.g., within five meters or ten meters ofthe monitoring device or within the same room for line-of-sightsignals). The inquiry request can be received by one or more userdevices within a signal range of the computing device (e.g., signalrange 160 in FIG. 1). The one or more user devices can respond to theinquiry request with a user device address, a user device name, and/or auser identifier. In some implementations, the monitoring device canconnect to the user devices, and the user devices can transmitinformation, such as a user device name and/or a user identifier, whileconnected to the monitoring device.

In some embodiments, the monitoring device can generate a timestampindicating or representing the time at which a user device was detected,and associate the timestamp with the user device information (e.g., theuser device address, the user device name, and/or the user identifier).

In various embodiments, 300 can be performed repeatedly and/orperiodically. For example, the monitoring device can transmit inquiryrequests every second, every 30 seconds, every minute, every fiveminutes, etc.

Additionally, in some implementations, in 300, the monitoring device candetermine which user devices are no longer within the signal range. Forexample, if the monitoring device transmits a first inquiry request at afirst time and receives a response from a user device and thensubsequently sends a second inquiry request at a second time and doesnot receive a response from the user device, then the monitoring devicecan record the lack of response and/or indicate or determine that theuser device has left its signal range. Accordingly, in some embodiments,the monitoring device can create or record data for each user devicethat includes both a timestamp associated with the first time the userdevice is identified and a timestamp associated with the firstsubsequent time when the user device does not respond to an inquiryrequest. Therefore, analysis of the data, including these timestamps, isable to deduce, conclude, or otherwise determine when particular usersare viewing content from a media device near or associated with themonitoring device and when users stop or are no longer viewing thecontent because they have left the vicinity of the media device (i.e.,are no longer viewing).

In further embodiments, the monitoring device can record multipletimestamps corresponding to a user device entering and leaving thesignal range. For example, the user of the device can leave and reenterthe room containing the media device numerous times within an hour orduring a TV show, or during an advertisement, to take breaks, get asnack, skip commercials, etc.

In some embodiments, the monitoring device can also record a signalstrength associated with a user device. For example, if a user device isclose to the monitoring device, the strength of amonitoring-device-received signal transmitted by the user device duringthe user device's response(s) can be relatively strong, and anindication of that strength can be recorded (e.g., 0.5-0.999 milliwattreceived signal strength for a 1 milliwatt user device transmitter). Asan additional example, if a user device is at the edge of the signalrange of the monitoring device, the strength of a received signaltransmitted during the user device's response can be relatively weak,and an indication of that strength can be recorded (e.g., 0.001-0.2milliwatt received signal strength).

As used herein, the data detected, generated, recorded, determined, etc.in 300 can be referred to as “user device data.” In 300, the monitoringdevice can store the user device data, and each record or entry in theuser device data can correspond to a detected user device.

In 310, the monitoring device can detect, via, for example, amicrophone, a camera, and/or a connection with a media device, contentbeing displayed, audibly played, or otherwise presented by the mediadevice. For example, the monitoring device 150 can detect content beingpresented by the media device 140 in FIG. 1.

In some embodiments, the content that is detected can be recorded asaudio or audiovisual files. In other embodiments, the detected contentinformation can be acoustic fingerprints and/or digital watermarksassociated with the content, which the monitoring device may record,store, and/or process. In further embodiments, the detected contentinformation can be an indication of a channel or station to which themedia device is tuned and a timestamp.

In some implementations, 310 can be performed repeatedly and/orperiodically. For example, the monitoring device can detect contentevery second, every 30 seconds, every minute, every five minutes, etc.

Additionally, in some implementations, the monitoring device may bedesigned such that it detects content only if at least one user deviceis identified in 300. Accordingly, if no user devices are detectedwithin a signal range of the monitoring device (i.e., if no users aredetectably watching or listening to the content) then 310 may not beperformed and the monitoring device can loop up (not shown) in theprocess of FIG. 3 to iteratively perform 300 again and then proceed to310 when at least one device is detected.

In some embodiments, the monitoring device may perform 300 only ifcontent is detected in 310 and/or may store user device data only ifcontent is detected in 310 (not shown in FIG. 3). For example, if amedia device is not presenting identifiably content (e.g., media devicesin the area of the monitoring device are turned off, are muted, and/orare presenting unrecognizable content), then no content may be detected(e.g., 310 is not performed by the monitoring device). In furtherembodiments, the monitoring device may detect that the media device isoff, is muted, or is presenting unrecognizable content and may stillperform 300. In such embodiments, the information stored about thecontent can indicate that there was no content, the content was muted,or that the content was unrecognizable.

In some embodiments, the monitoring device can detect, record, ordetermine a volume level of content being presented. For example, themonitoring device can determine a decibel level of the volume and/orclassify the volume as high or low or the like based on the decibellevel. In further embodiments, the monitoring device can additionallydetect, record, or determine if audiovisual content is muted. Forexample, if a camera of the monitoring device determines that visualcontent is being presented by a media device, but a microphone of themonitoring device cannot detect an audio signal, the monitoring devicecan record or store information indicating that the audiovisual contentis muted and/or has a very low or undetectable volume level, or thelike.

As used herein, the data detected, generated, recorded, determined, etc.in 310 can be referred to as “content data.” In 310, the monitoringdevice can store the content data, and each record or entry in thecontent data can correspond to a detected content item (e.g., an audiofile, an audiovisual file, an acoustic fingerprint, a digital watermark,a volume level, etc.).

In 320, the monitoring device can transmit monitoring data to a centralserver (e.g., server 240 in FIG. 2). In various embodiments, themonitoring data can include the user device data from 300 and thecontent data from 310. In further embodiments, the monitoring data canadditionally include a unique identifier of the monitoring device.

While the functions or operations depicted in FIG. 3 are shown as beingperformed in a particular order, the order described is merely anexample, and various different sequences of operations can be performed,consistent with certain disclosed embodiments. For example, as describedabove, 300 can be performed repeatedly until a user device is identifiedand then the process can proceed to 310 and then 320. Additionally, asan example, 310 can be performed repeatedly until recognizable contentis identified and then the process can proceed to 300 and then 320.Further, as an example, 300 and 310 can be performed repeatedly, userdevice and content data can be repeatedly stored, and then themonitoring device can send the data to the central server in batches,for example, at predetermined intervals (e.g., once per day, once perhour, once per 30 minutes, once per newly identified content, etc.).

Moreover, the operations are described in FIG. 3 as discrete stepsmerely for the purpose of explanation, and, in some embodiments,multiple operations may be performed simultaneously and/or as part of asingle computation. The operations described are not intended to beexhaustive, limiting, or absolute, and various operations can beinserted or removed.

FIG. 4 is a flow diagram illustrating an example of a process ofanalyzing monitoring data, consistent with certain disclosedembodiments. In various embodiments, the process can be performed usinga computing device (or multiple computing devices). For example, theprocess can be performed by server 240 in FIG. 2 after receivingmonitoring data transmitted or sent from one or more monitoring devices(e.g., 320 in FIG. 3). For another example, in some implementations,some or all of the operations of FIG. 4 may be performed by thecomputerized monitoring device that collected, recorded, created, orotherwise generated the monitoring data.

The example process can begin in 400 when the computing device receivesthe monitoring data from one or more monitoring devices. In variousembodiments, the monitoring data can include user device data andcontent data. In further embodiments, the monitoring data can include aunique identifier of the monitoring device that generated the monitoringdata.

In some embodiments, if the monitoring data does not include a uniqueidentifier of the monitoring device, the monitoring device can beidentified based on, for example, an Internet Protocol (IP) sourceaddress of a network data packet that includes the monitoring data(e.g., the IP address of the transmitting monitoring device).

In 410, the computing device can identify or determine users based onthe user device data. In some implementations, the computing device canhave access to a panel or roster of participating users (e.g., theroster is stored on the computing device). In some embodiments, theroster of participating users can be a table, database, or other datastructure that includes, for each participating user that has elected toparticipate in content viewership monitoring, a unique identifier of auser (e.g., an ID number, or unique username), demographic informationon a user (e.g., date of birth, gender, nationality, race, residenceaddress, occupation, level of education, income, etc.), and, optionally,the users' names, types of user devices, names of the user devices,addresses of the user devices, monitoring device identifiers, voicebiometrics, etc. In various embodiments, the roster of participatingusers can be populated using information received from the participatingusers during registration, described above.

In some implementations, the computing device can compare each record inthe user device data with the roster of participating users to identifyor determine the participating user associated with the user devicedata. For example, if the user device data includes a user deviceaddress, the computing device can search for the user device address inthe roster of participating users and identify which user is associatedwith that user device address. Similarly, the user can be identified ifthe user device data includes a username, a name of a user device, etc.

Additionally, in some embodiments, the computing device can identify ordetermine users based on data received from applications running on userdevices. For example, an application, running on a user device, canreceive an indication from a user that identifies the user and indicatesthat he or she is viewing and/or listening to a media device (e.g.,watching television) and generate a timestamp. The application on theuser device can then send the indication, the timestamp, and anidentifier of the user device either directly to the computing device(e.g. via a wireless Internet connection) or to a monitoring device thatpasses it to the computing device (e.g., to a server 240). The computingdevice may compare the information received from the user device to theuser device data received from a monitoring device to identify ordetermine user devices that were within a signal range of the monitoringdevice when the user indicated he or she was viewing or listening to amedia device based on the timestamps. Accordingly, the computing devicecan not only match the user to the user device used to record theindication, but can also match the user to other user devices within thesignal range of the monitoring devices. In other words, the computingdevice can deduce or conclude which user device(s) are carried by, usedby, or otherwise associated with a particular user by correlating thedevices that are in range at a given time with the user that reportsviewing/listening to the media device at the same or a similar time. Forexample, the computing device can use user device data and indicationsfrom a user that span multiple days, weeks, months, etc. and identifyeach device that is within a signal range of the monitoring device whenthe user indicates he or she is watching television. The computingdevice can then apply a clustering analysis to determine which userdevices are usually within the signal range when the user indicates heor she is watching television. The user devices that are usually withinthe signal range can also be deduced as being associated with the user.

In 420, the computing device can filter user device data based on a usernot being identified, timestamps, signal strength, etc. In variousembodiments, user device data that is “filtered” can be, for example,deleted or otherwise removed from consideration or further processing,or associated with an indicator that it was filtered and is to beanalyzed separately from user device data that is not filtered and/orused for clustering analyses, as described above.

In some embodiments, the user device data can include timestampscorresponding to when the consumer started watching or listening tocontent (i.e., when the user's device came within range of a monitoringdevice) and when the consumer stopped watching or listening to content(i.e., when the user's device went outside the range of a monitoringdevice). The user device data can include multiple sets of timestamps,indicating that the consumer left and reentered the signal range of themonitoring device numerous times.

In some implementations, the computing device can filter the user devicedata by determining that a user device was not within a signal range ofthe monitoring device for at least a threshold amount of time (e.g.,five minutes). For example, if the user device data included multiplesets of timestamps, the computing device can determine a total time thata user device was within a signal range of a monitoring device, anddetermine whether to filter the user device data based on the totaltime.

In other implementations, the computing device can filter the userdevice data when the computing device is unable to identify a user basedon the user device data. For example, the user device identified may beassociated with a user that has not elected to participate in thecontent viewership monitoring, such as, for example, a user device of anon-participating visitor to the home of a family participating in thecontent viewership monitoring. Accordingly, because the user has notelected to participate, the user will not have an entry in the roster ofparticipating users, and the user will not be identified.

In other implementations, the computing device can filter the userdevice data when the computing device determines that the signalstrength of the user device detected by a monitoring device was below aspecific threshold. For example, if a user device is close to themonitoring device, the recorded signal strength received during the userdevice's response can be relatively strong or high powered. Accordingly,the computing device can compare the signal strength to a specificthreshold (e.g. 0.2 milliwatt for a 1 milliwatt transmission) todetermine that the recorded strength of the signal is above thethreshold and not filter the user device data based on the signalstrength. As an additional example, if a user device is at the edge ofthe signal range of the monitoring device, the recorded signal strengthduring the user device's response can be relatively weak or low powered.Accordingly, the computing device can determine that the recordedstrength of the signal is below the threshold (e.g., below 0.2milliwatt) and filter out the user device data.

In 430, the computing device can identify content based on the contentdata. In some embodiments, the content data received in 400 can includeone or more of recorded audio or audiovisual files, acousticfingerprints, digital watermarks, identifiers or indications of achannel or station that a media device was tuned to and/or a timestamp.

In some implementations, the computing device can identify the contentusing ACR to identify acoustic fingerprints or digital watermarks in anaudio or audiovisual file, as described above.

In further implementations, the computing device can compare theacoustic fingerprints or digital watermarks to an audio database and/ora digital watermark database maintained by or in communication with thecomputing device. Accordingly, the computing device can match at leastone of the acoustic fingerprints or the digital watermarks to content.

In other implementations, the computing device can compare theindications of a channel or station that a media device was tuned to anda timestamp to a schedule of programming. For example, if the mediadevice was tuned to a specific television channel at a specific time(based on a channel indicator/identifier and the timestamp), thecomputing device can use a schedule of television programming todetermine which content was being presented (e.g., identify the TV showthat was broadcast) at that time on that television channel.

In 440, the computing device can match content identified in 430 tousers determined in 410 and not filtered in 420. As referred to herein,content matched to users can be referred to as “content viewershipdata.” For example, the computing device can match content to usersusing timestamps. In other words, a timestamp(s) indicating a userdevice associated with the user was within a signal range of themonitoring device at the same time as, or during the time period inwhich, content was being presented by a media device indicates that theuser was likely viewing the content being presented. As an additionalexample, content data can be associated with the user device data by themonitoring device that sent the data to the computing device.Accordingly, the computing device can match the user associated with theuser device data to the content associated with the correspondingcontent data.

In 450, the computing device can determine adjusted content viewershipdata, as discussed in further detail below. For example, the computingdevice can remove biases from the content viewership data, such as toomany users of a certain gender in the data, too many users of a certainage range in the data, etc., as described with regard to FIG. 7 below.

In 460, the computing device can generate a report of contentviewership. In some embodiments, the report can be generated based onthe viewership data determined in 440, and can indicate overallviewership of content, channels, stations, etc. based on theparticipants in the content viewership monitoring. In someimplementations, the viewership data in the report can be balanced usingthe adjusted content viewership data determined in 450.

In 470, the computing device can adjust advertising rates associatedwith content based on the report data. For example, if certain contentis not obtaining a desired viewership of a certain demographic, anadvertising rate associated with the content can be lowered. As anadditional example, if certain content is obtaining viewership numbersthat are higher than predicted viewership numbers, an advertising rateassociated with the content can be raised.

Additionally, in some embodiments, content viewership monitoring can beperformed in real time (i.e., receiving real time user device andcontent data from monitoring devices). Accordingly, in such embodiments,the computing device can facilitate addressable linear advertisementinsertion of advertisements within linear broadcasting channels bysending the report data to addressable linear advertising insertionservice providers. Addressable linear advertisement insertion allowstargeted advertisements to be overlaid on top of (or potentially inplace of) the linear broadcast stream. Accordingly, advertisementspresented to specific households, television sets, radios, etc. can becustomized based on, for example, geographic information, householddemographic information, etc. Additionally, the computing device canprovide real-time content viewership report data that indicates whichconsumers are watching television, listening to radio, etc. at a giventime. Therefore, addressable linear advertisement insertion serviceproviders can receive the real-time content viewership data from thecomputing device, and adjust advertisements not only based on householdinformation, but also based on which user is actively viewing content atthat moment.

While the operations depicted in FIG. 4 are shown as being performed ina particular order, the order described is merely an example, andvarious different sequences of operations can be performed, consistentwith certain disclosed embodiments. For example, 400 can be performedrepeatedly until enough data is received to proceed to 410.Additionally, as an example, 400 through 440 can be repeatedly performedeach time new monitoring data is received in 400, and then the computingdevice can proceed to 450 once enough data is received to determineadjusted content viewership data.

Moreover, the operations are described in FIG. 4 as discrete stepsmerely for the purpose of explanation, and, in some embodiments,multiple operations may be performed simultaneously and/or as part of asingle computation. The operations described are not intended to beexhaustive or absolute, and various operations can be inserted orremoved.

FIG. 5 is a flow diagram illustrating an example of a process ofmonitoring user devices and content and analyzing monitoring data,consistent with certain disclosed embodiments. In various embodiments,the process can be performed using a computerized monitoring device ordevices. For example, the process can be performed by monitoring device150 in FIG. 1 or monitoring device 210, monitoring device 220, ormonitoring device 230 in FIG. 2.

The example of a process can begin in 500, when the monitoring devicedetects one or more user devices. For example, the monitoring device candetect the one or more user devices as described above in 300 of FIG. 3.The data detected, generated, recorded, determined, etc. in 500 can bereferred to as “user device data.” In 500, the monitoring device canstore the user device data.

In 510, the monitoring device can identify or determine users based onthe user device data. For example, the monitoring device can identify ordetermine users as described above in 410 of FIG. 4. However, in someembodiments, unlike in 410 of FIG. 4, in 510, determining contentconsumers can be performed by a monitoring device, not a central server.According, like the central server in FIG. 4, the monitoring device canhave access to a roster of participating users and can compare the userdevice data detected in 500 to the roster of participating users todetermine the user associated with the user device data. In someembodiments, the determined user can be added to the user device data.

In 520, the monitoring device can filter user device data based on auser not being identified, timestamps, signal strength, etc. Forexample, the monitoring device can filter user device data as describedabove in 420 of FIG. 4. However, in some embodiments, unlike in 420 ofFIG. 4, in 520, filtering user device data can be performed by amonitoring device, not a central server. Accordingly, in someimplementations, if the monitoring device filters all of a current setof user device data (e.g., the monitoring device is unable to identifyany users using the user device data), the process can end and themonitoring device can return to 500 and detect one or more user devices(not shown in FIG. 5). In some embodiments, user device data that isfiltered can be removed from the user device data.

In 530, the monitoring device can detect content information via amicrophone, a camera, and/or a network connection with a media device.For example, the monitoring device can record audio or audiovisual filesand use ACR to identify acoustic fingerprints and/or digital watermarks,as described above in 310 of FIG. 3. As used herein, the data detected,generated, recorded, determined, etc. in 530 can be referred to as“content data.” In 530, the monitoring device can store the contentdata.

In 540, the monitoring device can identify content based on the contentdata, as described above in 430 of FIG. 4. However, in some embodiments,unlike in 430 of FIG. 4, in 540, identifying content can be performed bya monitoring device, not a central server. Accordingly, in variousembodiments, the monitoring device can have access to an audio databaseand/or a digital watermark database. In some embodiments, the contentidentifiers can be added to the content data.

In 550, the monitoring device can match content identified in 540 tousers determined in 510, as described above in 440 of FIG. 4. However,in some embodiments, unlike in 440 of FIG. 4, in 550, matching contentto users can be performed by a monitoring device, not a central server.As referred to herein, content matched to content consumers can bereferred to as “content viewership data.”

In 560, the monitoring device can send monitoring data to a centralserver (e.g., server 240 in FIG. 2). In various embodiments, themonitoring data can include the user device data from 520, the contentidentifiers from 540, and the content viewership data from 550. Infurther embodiments, the monitoring data can additionally include aunique identifier of the monitoring device.

In various embodiments, the central server can use the monitoring datato, for example, generate a report of content viewership, adjustadvertising rates, monitor content viewership in real time, facilitateaddressable linear advertisement insertion using real-time contentviewership data, etc.

While the operations depicted in FIG. 5 are shown as being performed ina particular order, the order described is merely an example, andvarious different sequences of operations can be performed, consistentwith certain disclosed embodiments. For example, 500 can be performedrepeatedly until a user device is identified and then the process canproceed to 510. Additionally, as an example, 530 can be performedrepeatedly until recognizable content is identified and then the processcan proceed to 540. Further, as an example, 500 and 530 can be performedrepeatedly, and user device data and content data can be repeatedlystored, and then the monitoring device can determine the contentviewership data in batches, for example, at predetermined intervals(e.g., once a day, once per hour, once per newly identified content,etc.).

Moreover, the operations are described in FIG. 5 as discrete stepsmerely for the purpose of explanation, and, in some embodiments,multiple operations may be performed simultaneously and/or as part of asingle computation. The operations described are not intended to beexhaustive or absolute, and various operations can be inserted orremoved.

FIG. 6 is a flow diagram illustrating an example of a process ofanalyzing monitoring data that includes content viewership data frommonitoring devices, consistent with certain disclosed embodiments. Invarious embodiments, the process can be performed using a computingdevice (or multiple computing devices). For example, the process can beperformed by server 240 in FIG. 2 after receiving monitoring data fromone or more monitoring devices (e.g., 320 in FIG. 3).

The example process can begin in 600 when the computing device receivesthe monitoring data from one or more monitoring devices. In variousembodiments, the monitoring data can include user device data, contentdata, and content viewership data. In further embodiments, themonitoring data can include a unique identifier of the monitoring devicethat generated the monitoring data.

In 610, the computing device can determine adjusted content viewershipdata based on the content viewership data, as discussed in furtherdetail below.

In 620, the computing device can generate a report of content viewershipbased on the content viewership data received in 600, as described abovein 460 of FIG. 4.

In 630, the computing device can adjust advertising rates associatedwith content based on the report data or facilitate addressable linearadvertisement insertion using real-time content viewership data, asdescribed above in 470 of FIG. 4.

While the operations depicted in FIG. 6 are shown as being performed ina particular order, the order described is merely an example, andvarious different sequences of operations can be performed, consistentwith certain disclosed embodiments. For example, 600 can be performedrepeatedly until enough data is received to proceed to 610.

Moreover, the operations are described in FIG. 6 as discrete stepsmerely for the purpose of explanation, and, in some embodiments,multiple operations may be performed simultaneously and/or as part of asingle computation. The operations described are not intended to beexhaustive or absolute, and various operations can be inserted orremoved.

FIG. 7 is a flow diagram illustrating an example of a process ofdetermining adjusted content viewership data, consistent with certaindisclosed embodiments. In various embodiments, the process can beperformed using a computing device (or multiple computing devices). Forexample, the process can be the process performed by server 240 in 450in FIG. 4 and/or the process performed by server 240 in 610 in FIG. 6,described above.

In 700, the computing device can obtain content viewership data. Forexample, the computing device can obtain the content viewership data byperforming 400-440 in FIG. 4, as described above. As an additionalexample, the computing device can receive the content viewership datafrom one or more monitoring devices, as described in 600 in FIG. 6.

In 710, the computing device can perform a modeling and/or an adjustmenttechnique on the content viewership data.

In some embodiments, the computing device can calculate an error rateand apply the error rate to the content viewership data. For example,the computing device can perform one or more methods and/or use one ormore systems described in U.S. Pat. No. 7,376,722 that issued 20 May2008, which is hereby incorporated by reference in its entirety.

In other embodiments, the computing device can determine one or moreadjustment factors based on the content viewership data and apply theone or more adjustment factors to the content viewership data. Forexample, the computing device can perform one or more methods and/or useone or more systems described in U.S. Pat. No. 8,626,901 that issued 7Jan. 2014, which is hereby incorporated by reference in its entirety.

In further embodiments, the computing device can determine a demographicdistribution of users associated with the content viewership data basedon assigned demographic values for a plurality of users that haveaccessed a particular entity. For example, the computing device canperform one or more methods and/or use one or more systems described inU.S. patent application Ser. No. 14/173,414 filed 5 Feb. 2014, which ishereby incorporated by reference in its entirety.

In other implementations, the computing device can use other knownmodeling and/or adjustment techniques to, for example, remove biasesfrom the content viewership data, such as too many users of a certaingender in the data, too many users of a certain age range in the data,etc.

In 720, the computing device can determine adjusted content viewershipdata. For example, the computing device can determine the adjustedcontent viewership data based on an error rate applied to the contentviewership data, based on one or more adjustment factors applied to thecontent viewership data, and/or based on based on assigned demographicvalues.

In various embodiments the adjusted content viewership data can be usedin generating a report on content viewership (e.g., as described withregard to 460 in FIG. 4).

While the operations depicted in FIG. 7 are shown as being performed ina particular order, the order described is merely an example, andvarious different sequences of operations can be performed, consistentwith certain disclosed embodiments.

Moreover, the operations are described in FIG. 7 as discrete stepsmerely for the purpose of explanation, and, in some embodiments,multiple operations may be performed simultaneously and/or as part of asingle computation. For example, in some embodiments, 710 and 720 can beperformed as part of a single computation. The operations described arenot intended to be exhaustive or absolute, and various operations can beinserted or removed.

FIG. 8 is a diagram illustrating an example of a hardware system forproviding a content and user monitoring system, consistent with certaindisclosed embodiments. An example hardware system 800 includes examplesystem components that may be used. The components and arrangement,however, may be varied.

Computer 801 may include processor 810, memory 820, storage 830, andinput/output (I/O) devices (not pictured). The computer 801 may beimplemented in various ways and can be configured to perform any of theembodiments described above. In some embodiments, computer 801 can be ageneral purpose computer of an end user such as, for example, a desktopcomputer, a laptop, a tablet device, a mobile device (e.g., asmartphone), etc. In other embodiments, computer 801 can be a computingdevice such as, for example, a database server, a web server, amainframe computer, etc. For example, computer 801 can be monitoringdevice 150 in FIG. 1 or monitoring device 210, monitoring device 220,monitoring device 230, or server 240 in FIG. 2. Computer 801 may bestandalone or may be part of a subsystem, which may, in turn, be part ofa larger system.

The processor 810 may include one or more known processing devices, suchas a microprocessor from the Intel Core™ family manufactured by Intel™,the Phenom™ family manufactured by AMD™, or the like. Memory 820 mayinclude one or more storage devices configured to store informationand/or instructions used by processor 810 to perform certain functionsand operations related to the disclosed embodiments. Storage 830 mayinclude a volatile or non-volatile, magnetic, semiconductor, tape,optical, removable, non-removable, or other type of computer-readablemedium used as a storage device. In some embodiments, storage 830 caninclude, for example, one or more of a roster of users, an audiodatabase, a digital watermark database, signal level thresholds, etc.

In an embodiment, memory 820 may include one or more programs orsubprograms including instructions that may be loaded from storage 830or elsewhere that, when executed by computer 801, and more specificallyby processor 810, perform various procedures, operations, or processesconsistent with disclosed embodiments. For example, memory 820 mayinclude content and user monitoring program 825 for detecting userdevices, detecting content, matching users to content, generatingreports of content viewership, adjusting advertising rates, etc.,according to various disclosed embodiments. Memory 820 may also includeother programs that perform other functions, operations, and processes,such as programs that provide communication support, Internet access,etc. The content and user monitoring program 825 may be embodied as asingle program, or alternatively, may include multiple sub-programsthat, when executed, operate together to perform the function of thecontent and user monitoring program 825 according to disclosedembodiments. In some embodiments, content and user monitoring program825 can perform all or part of the processes of FIGS. 3, 4, 5, 6, and/or7 described above.

Computer 801 may communicate over a link with network 840. For example,the link may be a direct communication link, a local area network (LAN),a wide area network (WAN), or other suitable connection. Network 840 mayinclude the internet, as well as other networks, which may be connectedto various systems and devices.

Computer 801 may include one or more input/output (I/O) devices (notpictured) that allow data to be received and/or transmitted by computer801. I/O devices may also include one or more digital and/or analogcommunication I/O devices that allow computer 801 to communicate withother machines and devices. I/O devices may also include input devicessuch as a keyboard or a mouse, and may include output devices such as adisplay or a printer. Computer 801 may receive data from externalmachines and devices and output data to external machines and devicesvia I/O devices. The configuration and number of input and/or outputdevices incorporated in I/O devices may vary as appropriate for variousembodiments.

Example uses of the system 800 can be described by way of example withreference to the embodiments described above.

While the teachings has been described with reference to the exampleembodiments, those skilled in the art will be able to make variousmodifications to the described embodiments without departing from thetrue spirit and scope. The terms and descriptions used herein are setforth by way of illustration only and are not meant as limitations. Inparticular, although the method has been described by examples, thesteps of the method may be performed in a different order thanillustrated or simultaneously. Furthermore, to the extent that the terms“including”, “includes”, “having”, “has”, “with”, or variants thereofare used in either the detailed description and the claims, such termsare intended to be inclusive in a manner similar to the term“comprising.” As used herein, the term “one or more of” with respect toa listing of items such as, for example, A and B, means A alone, Balone, or A and B. Those skilled in the art will recognize that theseand other variations are possible within the spirit and scope as definedin the following claims and their equivalents.

1. A system comprising: one or more processors; and a memory comprisingone or more non-transitory computer-readable media storing instructionsthat, when executed by the one or more processors, cause the system toperform operations comprising: transmitting a short-range wirelesssignal; detecting a user device that responds to the transmitting,wherein the user device transmits an indication that a user of the userdevice is using a media device; detecting content being presented by themedia device; storing monitoring data that comprises an indication ofthe user device and an indication of the content being presented; andtransmitting the monitoring data to a server, whereby the serverdetermines, using a clustering analysis, that the user is associatedwith the user device based on the indication from the user device. 2.The system of claim 1, wherein the server adjusts an advertising ratebased on the monitoring data.
 3. The system of claim 1, wherein themonitoring data is used for addressable linear advertisement insertion.4. The system of claim 1, wherein detecting the user device comprisesreceiving a user device identifier from the user device.
 5. (canceled)6. The system of claim 1, wherein detecting the content being presentedby the media device comprises detecting the content using one or more ofa microphone, a video camera, or a connection with the media device. 7.The system of claim 1, wherein detecting the content comprises recordingat least one of an audio file or an audiovisual file.
 8. The system ofclaim 7, wherein the operations further comprise identifying the contentusing automatic content recognition to detect at least one of anacoustic fingerprint or a digital watermark in the at least one of theaudio file or the audiovisual file.
 9. The system of claim 1, whereinthe short-range wireless signal comprises a radio frequency signal thatcorresponds to short-wavelength ultra-high frequency radio waves in theindustrial, scientific, and medical band from 2.4 to 2.485 gigahertz.10. The system of claim 1, wherein the operations further comprisefiltering the user device based on at least one or more of not beingable to determine a user corresponding to the user device, a timestampassociated with the user device, or a signal strength associated withthe user device.
 11. A system comprising: one or more processors; and amemory comprising one or more non-transitory computer-readable mediastoring instructions that, when executed by the one or more processors,cause the system to perform operations comprising: receiving monitoringdata from monitoring devices, wherein the monitoring data comprises:indications of user devices that were detected by the monitoring devicesusing a short-range wireless signal; and indications of contentpresented by media devices that was detected by the monitoring devices;receiving, from a subset of the user devices, indications that users ofthe subset of the user devices were using the media devices;determining, using a clustering analysis, that the users are associatedwith the user devices that were detected based on the indications fromthe subset of the user devices; identifying content based on theindications of the content detected by the monitoring devices; matchingthe users to the content using the monitoring data; and generating areport of content viewership based on the matching.
 12. The system ofclaim 11, wherein the operations further comprise adjusting anadvertising rate based on the report of content viewership.
 13. Thesystem of claim 11, wherein the report of content viewership is used foraddressable linear advertisement insertion.
 14. The system of claim 11,wherein the user devices were detected by receiving user deviceidentifiers from the user devices via the short-range wireless signal.15. (canceled)
 16. The system of claim 11, wherein the content wasdetected using microphones, video cameras, or connections with the mediadevices.
 17. The system of claim 11, wherein the content was detected byrecording at least one of an audio file or an audiovisual file.
 18. Thesystem of claim 17, wherein the operations further comprise identifyingthe content using automatic content recognition to detect at least oneof an acoustic fingerprint or a digital watermark in the at least one ofthe audio file or the audiovisual file.
 19. The system of claim 11,wherein the short-range wireless signal comprises a radio frequencysignal that corresponds to short-wavelength ultra-high frequency radiowaves in the industrial, scientific, and medical band from 2.4 to 2.485gigahertz.
 20. The system of claim 11, wherein the operations furthercomprise filtering the user devices based on at least one or more of atimestamp associated with a user device or a signal strength associatedwith a user device.
 21. The system of claim 1, wherein the operationsfurther comprise filtering the user device based on determining that anidentifier of the user device is not included in a roster ofparticipating users.
 22. The system of claim 11, wherein the operationsfurther comprise filtering the user devices based on determining thatthe users are not included in a roster of participating users.